The team proposes “Reverse ISP Estimation using Recurrent Dense Channel-Spatial Attention". This method is illustrated in Figure REF . Authors compare their method directly with CycleISP [1]} and improve considerably (+5dB).
The method was implemented in PyTorch 1.8 and trained for 2 days using a RTX 3090 GPU. MAD and SSIM were used as loss functions. The method has only 2.8M parameters (724 GFLOP) and it can unprocess a 252px RGB in 10ms (using GPU).
This method can generalize to unseen sensors as shown in Table REF , however, authors detected a bias towards indoor images.
